sitar: pdLMS – R documentation

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pdLMS

Plot frequency distributions(s) for given L, M and S values in LMS method

Description

The LMS method defines frequency distributions in terms of L, M and S parameters. pdLMS plots one or more LMS distributions and optionally returns specified centiles on each distribution.

Usage

pdLMS(
  L = 1,
  M = 1,
  S = 0.2,
  zcent = NULL,
  zlim = 3.5,
  N = 1000,
  plot = TRUE,
  ...
)

Arguments

`L`	vector of Box-Cox transformation (lambda) values, L in the LMS method (default 1 corresponding to the Normal distribution).
`M`	vector of medians (mu), M in the LMS method (default 1).
`S`	vector of coefficients of variation (sigma), S in the LMS method (default 0.2).
`zcent`	optional vector of z-scores for conversion to the measurement scale under each distribution.
`zlim`	scalar defining z-score limits underlying x-axis (default 3.5).
`N`	number of points per distribution curve (default 1000).
`plot`	logical for plotting (default TRUE).
`...`	Further graphical parameters (see `par`) may also be supplied as arguments, particularly colour `col`, line type `lty`, line width `lwd` and character `pch`.

Details

L, M and S should all be the same length, recycled if necessary.

Value

An invisible list with the following components:

`x`	vector of x values for plotting.
`density`	matrix of densities for each distribution.
`centile`	matrix of measurement centiles corresponding to `zcent` under each distribution.

The distributions can be plotted with matplot(x, density, type='l').

Author(s)

Tim Cole tim.cole@ucl.ac.uk

Examples

## plot normal distribution
pdLMS()
## compare variety of distributions
## with centiles corresponding to +3 z-scores
pdLMS(L=-2:3, M=2:3, S=1:3/10, zcent=3, lty=1)

sitar

Super Imposition by Translation and Rotation Growth Curve Analysis

v1.2.0

GPL (>= 2)

Authors

Tim Cole [aut, cre] (<https://orcid.org/0000-0001-5711-8200>)

Initial release